Summary of Joint Multimodal Transformer For Emotion Recognition in the Wild, by Paul Waligora et al.
Joint Multimodal Transformer for Emotion Recognition in the Wild
by Paul Waligora, Haseeb Aslam, Osama Zeeshan, Soufiane Belharbi, Alessandro Lameiras Koerich, Marco Pedersoli, Simon Bacon, Eric Granger
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multimodal emotion recognition (MMER) method is proposed, leveraging the complementary relationships between various modalities such as visual, textual, physiological, and auditory cues. The joint multimodal transformer (JMT) fusion architecture captures intra-modal dependencies within each modality and integrates them to effectively recognize emotions. Experimental results on two challenging tasks demonstrate that MMER systems with JMT fusion outperform baseline and state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to recognize emotions by combining different types of signals, like facial expressions, voice tone, and physiological responses. The system uses special computer models to analyze these signals together, which helps it make more accurate predictions about how someone is feeling. By testing this method on two difficult tasks, the researchers showed that it can be a cost-effective way to recognize emotions. |
Keywords
* Artificial intelligence * Transformer
